dev(narugo): save current code

.gitignore

@@ -1225,3 +1225,6 @@ fabric.properties
 /YOLOv8
 .tests
 /pretrained_models
+/*.wav
+/mdist
+/onnxs

report_dynamo_export.sarif

@@ -0,0 +1,26 @@
+{
+ "runs":[
+  {
+   "tool":{
+    "driver":{
+     "name":"torch.onnx.dynamo_export",
+     "contents":[
+      "localizedData",
+      "nonLocalizedData"
+     ],
+     "language":"en-US",
+     "rules":[],
+     "version":"2.4.0+cu121"
+    }
+   },
+   "language":"en-US",
+   "newlineSequences":[
+    "\r\n",
+    "\n"
+   ],
+   "results":[]
+  }
+ ],
+ "version":"2.1.0",
+ "schemaUri":"https://docs.oasis-open.org/sarif/sarif/v2.1.0/cs01/schemas/sarif-schema-2.1.0.json"
+}

requirements-test.txt

@@ -16,4 +16,5 @@ pytest-image-diff>=0.0.11
 matplotlib
 natsort
 torchaudio
-torch
+torch
+transformers

requirements-zoo.txt

@@ -16,3 +16,5 @@ pyarrow
 pandas
 langdetect
 pyquery
+onnxscript
+transformers

soundutils/data/__init__.py

@@ -1 +1,3 @@
+from .mel import mel_to_hertz, hertz_to_mel
+from .octave import hertz_to_octave
 from .sound import Sound, SoundTyping

soundutils/data/mel.py

@@ -0,0 +1,125 @@
+# coding=utf-8
+# Copyright 2023 The HuggingFace Inc. team and the librosa & torchaudio authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+This module provides functions for converting between Hertz and Mel frequency scales.
+
+The Mel scale is a perceptual scale of pitches judged by listeners to be equal in distance from one another.
+This scale is often used in speech processing and audio analysis tasks.
+
+The module supports three different Mel scale implementations:
+1. HTK (Hidden Markov Model Toolkit)
+2. Kaldi (Speech Recognition Toolkit)
+3. Slaney (Malcolm Slaney's implementation)
+
+Functions:
+- hertz_to_mel: Convert frequency from Hertz to Mel scale
+- mel_to_hertz: Convert frequency from Mel scale to Hertz
+
+These functions support both single float values and numpy arrays as inputs.
+"""
+
+from typing import Union
+
+import numpy as np
+
+
+def hertz_to_mel(freq: Union[float, np.ndarray], mel_scale: str = "htk") -> Union[float, np.ndarray]:
+    """
+    Convert frequency from hertz to mels.
+
+    This function transforms frequencies from the Hertz scale to the Mel scale.
+    The Mel scale is a perceptual scale of pitches judged by listeners to be equal in distance from one another.
+
+    :param freq: The frequency, or multiple frequencies, in hertz (Hz).
+    :type freq: float or np.ndarray
+    :param mel_scale: The mel frequency scale to use. Options are "htk", "kaldi", or "slaney".
+    :type mel_scale: str, optional
+    :return: The frequencies on the mel scale.
+    :rtype: float or np.ndarray
+    :raises ValueError: If mel_scale is not one of "htk", "slaney", or "kaldi".
+
+    :Example:
+
+    >>> hertz_to_mel(1000)
+    1000.0
+    >>> hertz_to_mel(np.array([440, 880]))
+    array([548.68, 968.31])
+    """
+
+    if mel_scale not in ["slaney", "htk", "kaldi"]:
+        raise ValueError('mel_scale should be one of "htk", "slaney" or "kaldi".')
+
+    if mel_scale == "htk":
+        return 2595.0 * np.log10(1.0 + (freq / 700.0))
+    elif mel_scale == "kaldi":
+        return 1127.0 * np.log(1.0 + (freq / 700.0))
+
+    min_log_hertz = 1000.0
+    min_log_mel = 15.0
+    logstep = 27.0 / np.log(6.4)
+    mels = 3.0 * freq / 200.0
+
+    if isinstance(freq, np.ndarray):
+        log_region = freq >= min_log_hertz
+        mels[log_region] = min_log_mel + np.log(freq[log_region] / min_log_hertz) * logstep
+    elif freq >= min_log_hertz:
+        mels = min_log_mel + np.log(freq / min_log_hertz) * logstep
+
+    return mels
+
+
+def mel_to_hertz(mels: Union[float, np.ndarray], mel_scale: str = "htk") -> Union[float, np.ndarray]:
+    """
+    Convert frequency from mels to hertz.
+
+    This function transforms frequencies from the Mel scale back to the Hertz scale.
+    It is the inverse operation of hertz_to_mel.
+
+    :param mels: The frequency, or multiple frequencies, in mels.
+    :type mels: float or np.ndarray
+    :param mel_scale: The mel frequency scale to use. Options are "htk", "kaldi", or "slaney".
+    :type mel_scale: str, optional
+    :return: The frequencies in hertz.
+    :rtype: float or np.ndarray
+    :raises ValueError: If mel_scale is not one of "htk", "slaney", or "kaldi".
+
+    :Example:
+
+    >>> mel_to_hertz(1000)
+    1000.0
+    >>> mel_to_hertz(np.array([548.68, 968.31]))
+    array([440., 880.])
+    """
+
+    if mel_scale not in ["slaney", "htk", "kaldi"]:
+        raise ValueError('mel_scale should be one of "htk", "slaney" or "kaldi".')
+
+    if mel_scale == "htk":
+        return 700.0 * (np.power(10, mels / 2595.0) - 1.0)
+    elif mel_scale == "kaldi":
+        return 700.0 * (np.exp(mels / 1127.0) - 1.0)
+
+    min_log_hertz = 1000.0
+    min_log_mel = 15.0
+    logstep = np.log(6.4) / 27.0
+    freq = 200.0 * mels / 3.0
+
+    if isinstance(mels, np.ndarray):
+        log_region = mels >= min_log_mel
+        freq[log_region] = min_log_hertz * np.exp(logstep * (mels[log_region] - min_log_mel))
+    elif mels >= min_log_mel:
+        freq = min_log_hertz * np.exp(logstep * (mels - min_log_mel))
+
+    return freq

soundutils/data/octave.py

@@ -0,0 +1,26 @@
+from typing import Union, Optional
+
+import numpy as np
+
+
+def hertz_to_octave(
+        freq: Union[float, np.ndarray], tuning: Optional[float] = 0.0, bins_per_octave: Optional[int] = 12
+):
+    """
+    Convert frequency from hertz to fractional octave numbers.
+    Adapted from *librosa*.
+
+    Args:
+        freq (`float` or `np.ndarray`):
+            The frequency, or multiple frequencies, in hertz (Hz).
+        tuning (`float`, defaults to `0.`):
+            Tuning deviation from the Stuttgart pitch (A440) in (fractional) bins per octave.
+        bins_per_octave (`int`, defaults to `12`):
+            Number of bins per octave.
+
+    Returns:
+        `float` or `np.ndarray`: The frequencies on the octave scale.
+    """
+    stuttgart_pitch = 440.0 * 2.0 ** (tuning / bins_per_octave)
+    octave = np.log2(freq / (float(stuttgart_pitch) / 16))
+    return octave

soundutils/preprocess/transformers/__init__.py

@@ -0,0 +1 @@
+from .whisper import WhisperFeatureExtractor